Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities.
Tenstorrent is building next-generation CPU and AI silicon, supported by the infrastructure needed to develop, validate, and deploy advanced AI compute systems. As a Site Reliability Engineer / Infrastructure Operations Engineer, you will help maintain the health and reliability of our infrastructure by monitoring systems, investigating alerts, coordinating incident resolution, and improving operational processes. You will work across Linux systems, servers, compute clusters, and supporting infrastructure, partnering with engineering teams to identify issues, resolve failures, and prevent recurring problems.
This role is focused on infrastructure reliability and operational execution, with opportunities to improve monitoring, automation, troubleshooting, and recovery practices as our infrastructure scales.
This role is remote, based out of North America.
We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.
Who You Are
- Experienced in SRE, infrastructure operations, systems administration, production support, or a related technical operations role.
- Comfortable working with Linux systems and server infrastructure, with strong troubleshooting skills and a methodical approach to investigating unfamiliar issues.
- Familiar with monitoring, alerting, dashboards, and system health metrics, with the ability to distinguish actionable incidents from routine fluctuations.
- Organized and detail-oriented, with experience maintaining technical documentation, operational procedures, and troubleshooting runbooks.
- A clear communicator and collaborative problem-solver who takes ownership of issues, follows through on escalations, and looks for ways to improve reliability.
What We Need
- Monitor the availability, performance, and capacity of infrastructure across servers, compute clusters, and supporting systems, investigating alerts and anomalies as they arise.
- Triage incidents by assessing severity and impact, gathering diagnostic information, escalating issues to the appropriate engineering teams, and tracking them through resolution.
- Develop, maintain, and execute operational runbooks for troubleshooting, incident response, system recovery, and routine maintenance.
- Improve monitoring, dashboards, and alerting to increase infrastructure visibility, reduce alert noise, and enable earlier detection of potential failures.
- Analyze recurring incidents and operational gaps, collaborating with infrastructure and engineering teams to identify root causes, improve recovery procedures, and prevent repeat issues.
What You Will Learn
- Gain hands-on experience supporting the infrastructure behind next-generation AI compute platforms, including servers, compute clusters, and large-scale systems.
- Deepen your understanding of Linux, hardware management, networking, storage, and the interactions between hardware and software in production environments.
- Build expertise in monitoring and observability tools, incident management, root-cause analysis, and infrastructure reliability practices.
- Develop practical skills in scripting and automation using tools such as Python, Bash, and Ansible to streamline troubleshooting and routine operations.
- Collaborate with infrastructure and engineering teams to improve operational maturity, strengthen incident response, and build more reliable and scalable AI compute infrastructure.
Compensation for all engineers at Tenstorrent ranges from $100k - $500k including base and variable compensation targets. Experience, skills, education, background and location all impact the actual offer made.
Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.
This offer of employment is contingent upon the applicant being eligible to access U.S. export-controlled technology. Due to U.S. export laws, including those codified in the U.S. Export Administration Regulations (EAR), the Company is required to ensure compliance with these laws when transferring technology to nationals of certain countries (such as EAR Country Groups D:1, E1, and E2). These requirements apply to persons located in the U.S. and all countries outside the U.S. As the position offered will have direct and/or indirect access to information, systems, or technologies subject to these laws, the offer may be contingent upon your citizenship/permanent residency status or ability to obtain prior license approval from the U.S. Commerce Department or applicable federal agency. If employment is not possible due to U.S. export laws, any offer of employment will be rescinded.
Tenstorrent Inc. Santa Clara, California, USA Office
Santa Clara, California, United States
Similar Jobs
What you need to know about the San Francisco Tech Scene
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
